Sukhairi Sudin

Track Cyclist Performance Monitoring System Using Wireless Sensor Network

The right training programs are an important factor to increase the cycling performance among the professional track cyclist. Over the years, the cyclist performance was based on the feedback from bicycle’s kinematics and physiological condition. The advancement in sensor technologies allows the optimization of the training program; by combining both information from the cyclist’s physiological condition and kinematic data from the bicycle. The physiological conditions such as heart rate variability (HRV) and forehead temperate can be combined with bicycle kinematic data such as speed and distance to provide accurate assessment of the track cyclist’s condition and training program intensity. A system that combines data from physiological signal and bicycle kinematic has been developed for this purpose. Wearable physiological body sensors and bicycle kinematic sensors are deployed using wireless sensor network (WSN). HRV provide using photoplethysmography (PPG) technique that capture signal from cyclist’s finger, which provide 3 % error rate refer to heart rate belt. Data handling and communication was developed based on Zigbee protocol whereby the WSN centralized base-station was supported by two repeater node which was used to extend signal coverage in Velodrome to prevent data losses. With two repeater nodes and adjustment on the routing protocol, the packet drops were reduced from 46 to 3 %. The propagation study was carried out in the Velodrome with environment temperature range from 28 to 30 °C and humidity was observed at 85 %. The optimization of network topology by considering the connectivity among the wireless nodes is crucial in order to reduce data losses.

Real-time track cycling performance prediction using ANFIS system

ABSTRACT The next stage performance evaluation of an athlete can be predicted by implementing Artificial Intelligence technique. In track cycling event, coach and sports physician are concerned with the performance of the cyclist. The performance prediction may help to fine-tune the cyclist training intensities and strategies planning. This study was conducted to fulfil the prediction requirement by adopting a Fuzzy Inference System to classify the cyclist current cycling performance state. The six levels of output classification by a Fuzzy Inference System are to indicate the athlete’s current state performance using the body temperature, heart rate variability and speed as input parameters. An Adaptive Neuro-Fuzzy Inference System was applied to predict the cycling speed that can be achieved in the next lap. Using Adaptive Neuro-Fuzzy Inference System method, the average speed for the next laps can be predicted and compared with the actual speed. The regression value with r = 0.9029 indicates the Adaptive Neuro-Fuzzy Inference System is an adequate prediction algorithm to evaluate the cyclist performance. The predicted time to complete compared favourably with the actual finishing time with a ± 13.6% average error. Hence, the developed system is reliable and suitable for sports events that deal with speed and time.

Wearable heart rate monitor using photoplethysmography for motion

Heart rate variability (HRV) extracted from human physiological turn to be important data as indicators of human health. Originally, HRV calculated and monitored from Electrocardiography (ECG) signal. This research proved that photoplethysmography (PPG) as a simple non-invasive method can measure HRV in motion condition. PPG used Light Photo Sensor (LPS) to capture light reflectance intensity on the finger. The signal is sampled, filtered, processed and sent wirelessly through ZigBee protocol. The system applied on glove to make it wearable and easy to wear. Experimental results show that the system functioning properly with HRV received, displayed and recorded by the base station.

Adjustable Crank: A Comparison Between Wireless Motion Sensor and Motion Capture Analysis Camera for Crank Kinematic Measurement

This paper is focused on the development of wireless measured kinematics specifically for cycling. The aims of this study are to create sensory system with portability, reliability and based on the wireless system. The adjustable crank is novel type of prototype crank that design to maximize the minimum torque at bottom dead center (BDC) and top dead center (TDC) where the crank can be set for ±10° maximum with addition of 5° back and forth from inertial 0° TDC point. The system will measure the power output during cycling to evaluate the cyclist performance. In order to measure power output, the angle displacement (kinematic) and force measurement (kinetic) are needed. The inertial measurement unit (IMU) combination of accelerometers and gyrometers was used to measure the angle and angular velocity of the crank. The system has been validated using a visual system to compare the output provided by IMU. The RMS error value between motion capture camera and IMU for crank angle was 0.480 ± 0.325°. The RMS error value for normalized angular velocity was 0.743 ± 0.911 %. The wireless-based system will aid to reduce the wiring complexity and user-friendly portable measuring system. The wireless communication using Zigbee protocol with two Xbee devices point-to-point will be used to transfer the information to the computer controlled system. The enhancement of this system can be used for coaches for cycling monitoring system to improve cyclist coordination, strategy, and technique.

Athlete Overtraining Monitoring System

This paper introduces the design and development of a system that can early detect overtraining problem during training activities. These problems can affect athletes’ physiological and psychological conditions as well as reducing their performance. Maximum heart rate (MHR) is the limitation heart rate of athlete and can be indicator for overtraining exercise. Heart rate is usually used to detect and prevent overtraining by coaches and athlete. In this study, we use electrocardiograph (ECG) for amplifying and filtering the signal from the body. National instrument (NI) DAQ is used to acquire the real-time signal from the sensors circuit and pass the data to LabVIEW for real-time monitoring and analysis. The software displays heart rate as well as detecting the abnormality if present. Furthermore, it also features a simple yet comprehensive user interface where the athlete data, date and time for the data collection are saved in the specified txt file for future reference.